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Short Courses

Over the course of the year Stats Central teaches several short courses aimed at researchers across all disciplines. Offerings in 2018 include:

Introduction to R

Introductory Statistics for Researchers

Experimental Design

Introduction to Regression Modelling in R

Introductory Statisitcs for Researchers

5-6 April, 2018

Stats Central at UNSW Sydney is conducting a short course, Introductory Statisitcs for Researchers, in April 2018. Aimed at research workers, the course provides an overview of statistical design and analysis methods. The course emphasises understanding the concepts underlying statistical procedures (relying on a minimum of mathematics) and interpreting the output from statistical analyses. The statistical package used in the course is R (click here for more information). Please note if you are unfamiliar with R a one day Introduction to Ris being run on 4 April, 2018 (see below).

Introductory Statistics for Researchers - April, 2018

In many disciplines, researchers wishing to publish are asked to provide a rigorous statistical analysis. Reviewers are often specific about what statistical measures they want included. "Why wasn't Fisher's Exact Test used?" "Was an appropriate sample size determined a priori?"

Statistical analyses require specialised software to perform calculations. In this course, we use the free statistical program R, although researchers may have another statistical package available to them.

How does one decide which statistical procedure is the most appropriate? What do all the pages of the printout mean?

This course is designed as an introduction to statistical design and analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher.

Instructions on how to obtain computer printouts will be provided with an emphasis on interpreting the computer printout (most packages produce similar printouts). There will be plenty of practical work throughout the course.

Do you need to have previous experience using the program R?

It would be very helpful if you have some basic knowledge of R. We recommend our one-day course,Introduction to R (see below), which runs just prior to this course. However, previous experience with statistical packages like SAS, SPSS or Stata and some basic programming skills are also helpful.

Please note that R and R Studio are free to use and are both available for Windows/Mac/Linx platforms. The links are provided below:

Non-Normal data - transformations and non-parametric tests. If time is available, we will briefly discuss sample size and power.

Duration: 2 days, 9.00am to 5.00pm daily

Dates: Thursday 5th to Friday 6th April, 2018

Cost

UNSW students: $200

UNSW staff: $400

External: $1,000

Requirements:

Bring your own laptop computer.

This Short Course is based on intellectual property developed by the School of Mathematics and Statistics.

Introduction to R

4 April, 2018

R is widely used and extremely powerful statistical software. This course assumes that you have never used R before. You will learn how to obtain and install R, which is open-source software, and RStudio, which is a versatile, user-friendly interface for using R.

It is very useful to do this course before our introductory statistics course, Introductory Statistics for Researchers (see above).

This one-day introduction to R will cover some basic features of R and lay the groundwork for you to improve your R skills independently. The course is self-paced and focussed on developing practical skills.

types of data - different types of data structures and how to store them (e.g. numbers, text and Boolean (TRUE or FALSE) values)

organising R code and data so you can easily reuse them at a later date (script files and working directories)

efficient ways to create patterns of numbers

logical operators useful for manipulating data (e.g. <, >, etc.)

handling "spreadsheets" in R (matrices and data frames)

adding comments to your code so people you share code with can easily follow it

using inbuilt R Help files and other help resources

Duration: 1 day, 9.00 am to 5.00 pm

Date: Wednesday, 4 April 2018

Cost:

UNSW students: $100

UNSW staff: $200

External: $500

Advanced Statistical Methods in Epidemiology

6-7 November, 2017

This workshop is aimed at health researchers and practitioners who are familiar with basic statistical methods used in epidemiology and who would like to expand their knowledge and skills in this area.

The workshop will use one or more existing epidemiological data sets to illustrate how to use software to conduct statistical analyses and interpret statistical results.

Note 1: In contrast to clinical trials, case-control studies are observational in nature. In an observational study, the role of the investigator is passive: they observe the individuals and collect relevant data, but do not influence the course of events.

Note 2: It is assumed participants are familiar with the basic conduct of systematic reviews but not yet experienced with conducting meta-analyses.

Software used in the course: R, RStudio, metafor package for R.

Duration: 2 days, 9.00 am to 5.00 pm daily

Dates: 6-7 November, 2017

Cost

UNSW Students: $200

UNSW Staff: $400

External (no UNSW affiliation): $1,000

Additional notes

Participants will be given workshop slides, R code and data shortly before the course.

Please bring your own laptop computer to the course.

Course fees include morning and afternoon teas and lunch on both days.

Introductory Statistics for Researchers

26-27 September, 2017

Stats Central and the School of Mathematics and Statistics at UNSW are jointly conducting a short course, Introductory Statisitcs for Researchers, in September 2017. Aimed at research workers, the course provides an overview of statistical design and analysis methods. The course emphasises understanding the concepts underlying statistical procedures (relying on a minimum of mathematics) and interpreting the output from statistical analyses. The statistical package used in the course is R (click here for more information). Please note if you are unfamiliar with R a one day Introduction to Ris being run on 25 September, 2017 (see above).

Introductory Statistics for Researchers - September 2017

In many disciplines, researchers wishing to publish are asked to provide a rigorous statistical analysis. Reviewers are often specific about what statistical measures they want included. "Why wasn't Fisher's Exact Test used?" "Was an appropriate sample size determined a priori?"

Statistical analyses require specialised software to perform calculations. In this course, we use the free statistical program R, although researchers may have another statistical package available to them.

How does one decide which statistical procedure is the most appropriate? What do all the pages of the printout mean?

This course is designed as an introduction to statistical design and analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher.

Instructions on how to obtain computer printouts will be provided with an emphasis on interpreting the computer printout (most packages produce similar printouts). There will be plenty of practical work throughout the course.

Do you need to have previous experience using the program R?

It would be very helpful if you have some basic knowledge of R. We recommend our one-day course,Introduction to R (see above), which runs just prior to this course. However, previous experience with statistical packages like SAS, SPSS or Stata and some basic programming skills are also helpful.

Please note that R and R Studio are free to use and are both available for Windows/Mac/Linx platforms. The links are provided below:

Non-Normal data - transformations and non-parametric tests. If time is available, we will briefly discuss sample size and power.

Duration: 2 days, 9.00am to 5.00pm daily

Dates: 26 - 27 September, 2017

Cost

UNSW students: $200

UNSW staff: $400

External: $1,000

Requirements:

Bring your own laptop computer.

This Short Course is based on intellectual property developed by the School of Mathematics & Statistics.

Introduction to R

25 September, 2017

R is widely used and extremely powerful statistical software. This course assumes that you have never used R before. You will learn how to obtain and install R, which is open-source software, and RStudio, which is a versatile, user-friendly interface for using R.

It is very useful to do this course before our introductory statistics course, Introductory Statistics for Researchers (see below).

This one-day introduction to R will cover some basic features of R and lay the groundwork for you to improve your R skills independently. The course is self-paced and focussed on developing practical skills.

types of data - different types of data structures and how to store them (e.g. numbers, text and Boolean (TRUE or FALSE) values)

organising R code and data so you can easily reuse them at a later date (script files and working directories)

efficient ways to create patterns of numbers

logical operators useful for manipulating data (e.g. <, >, etc.)

handling "spreadsheets" in R (matrices and data frames)

adding comments to your code so people you share code with can easily follow it

using inbuilt R Help files and other help resources

Duration: 1 day, 9.00 am to 5.00 pm

Date: Monday 25 September, 2017

Cost:

UNSW students: $100

UNSW staff: $200

External: $500

Introduction to regression modelling in R

19-21 June, 2017

The core outcome from this course is to recognise that most statistical methods you use can be understood under a single framework, as special cases of (generalised) linear models. Learning statistical methods in a systematic way, instead of as a "cookbook" of different methods, enables you to take a systematic approach to key steps in analysis (like assumption checking) and to extend your skills to handle more complex situations you might encounter in the future (random factors, multivariate analysis, choosing between a set of competing models).

This three-day short course is aimed at applied researchers with prior experience using R and familiar with introductory statistics tools - you should know about the t-test, linear regression, analysis of variance and know something about orthogonal and nested designs. If you have not used R before, we strongly recommend you attend the Introduction to R course on 13 June. If you need to revise introductory statistics material, you should attend the Introductory Statistics course on 14-15 June prior to taking the regression course, which will take such material as assumed knowledge.

NOTE: If you have not used R before, we strongly recommend you attend the Introduction to R course on June 13.

Make sure you bring your own laptop! We will sort out internet access for you.

Course fees include morning and afternoon teas and lunches on all days.

Requirements:

Bring your own laptop computer.

Model-based multivariate analysis of abundance data using R

22-26 November, 2016

Multivariate analysis in ecology has been changing rapidly in recent years, with a focus now on formulating a statistical model to capture key properties of the observed data, rather than transformation of data using a dissimilarity-based framework. In recent years, model-based techniques have been developed for hypothesis testing, identifying indicator species, ordination, clustering, predictive modelling, and use of species traits as predictors to explain interspecific variation in environmental response. These techniques are more interpretable than alternatives, have better statistical properties, and can be used to address new problems, such as the prediction of a species’ spatial distribution from its traits alone.

This course will provide an introduction to modern multivariate techniques, with a special focus on the analysis of abundance or presence/absence data, starting from a revision of fundamental tools in regression analysis, and extending these techniques to the case where there are multiple response variables.

Experimental Design

20 April, 2017

This is a one day short cousre which covers the essentials of Experimental Design. Topics covered include randomisation, controls, sample size, reducing variability and pilot studies. This course will also have a practical component using online tools, excel and G*Power.